Spontaneous brain activity in the hippocampal regions could characterize cognitive impairment in patients with Parkinson's disease

Abstract Objective This study aimed to investigate whether spontaneous brain activity can be used as a prospective indicator to identify cognitive impairment in patients with Parkinson's disease (PD). Methods Resting‐state functional magnetic resonance imaging (RS‐fMRI) was performed on PD patients. The cognitive level of patients was assessed by the Montreal Cognitive Assessment (MoCA) scale. The fractional amplitude of low‐frequency fluctuation (fALFF) was applied to measure the strength of spontaneous brain activity. Correlation analysis and between‐group comparisons of fMRI data were conducted using Rest 1.8. By overlaying cognitively characterized brain regions and defining regions of interest (ROIs) based on their spatial distribution for subsequent cognitive stratification studies. Results A total of 58 PD patients were enrolled in this study. They were divided into three groups: normal cognition (NC) group (27 patients, average MoCA was 27.96), mild cognitive impairment (MCI) group (21 patients, average MoCA was 23.52), and severe cognitive impairment (SCI) group (10 patients, average MoCA was 17.3). It is noteworthy to mention that those within the SCI group exhibited the most advanced chronological age, with an average of 74.4 years, whereas the MCI group displayed a higher prevalence of male participants at 85.7%. It was found hippocampal regions were a stable representative brain region of cognition according to the correlation analysis between the fALFF of the whole brain and cognition, and the comparison of fALFF between different cognitive groups. The parahippocampal gyrus was the only region with statistically significant differences in fALFF among the three cognitive groups, and it was also the only brain region to identify MCI from NC, with an AUC of 0.673. The paracentral lobule, postcentral gyrus was the region that identified SCI from NC, with an AUC of 0.941. The midbrain, hippocampus, and parahippocampa gyrus was the region that identified SCI from MCI, with an AUC of 0.926. Conclusion The parahippocampal gyrus was the potential brain region for recognizing cognitive impairment in PD, specifically for identifying MCI. Thus, the fALFF of parahippocampal gyrus is expected to contribute to future study as a multimodal fingerprint for early warning.

Parkinson's disease (PD) is a clinically common neurodegenerative disease characterized by motor symptoms, including tremors, bradykinesia, and rigidity.In addition to motor symptoms, nonmotor symptoms are considered to precede motor symptoms, affecting the quality of life. 1,2Common non-motor symptoms of PD include dementia, neuropsychiatric symptoms, autonomic failure, and sensory impairments.Dementia is most detrimental to the quality of life and increases mortality rates. 3,4Mild cognitive impairment (MCI) is considered a transitional stage between normal state and full-blown dementia and is also a powerful predictor of dementia. 5,68][9] Therefore, the research on PD-MCI is increasing rapidly, and exploring the characteristics of different cognitive states in PD and the laws of transition between states is the focus of disease pathology research, especially finding the predictors of PD-MCI to provide the theoretical reference for early treatment and rehabilitation.
The research found that demographic indicators of gender were a critical factor, that men were more likely to develop cognitive impairment, and that characteristics of cognitive impairment differed between genders. 10Other factors included genotype, in which the Catechol-O-methyltransferase genotype was related to executiveattention function 11,12 and the apolipoprotein E genotype was related to cognitive decline. 13 the study of radiological imaging predictive factors, the cross-sectional analysis suggested that PD-MCI was more likely to develop brain atrophy, occurring in areas such as the left superior frontal gyrus, superior temporal lobe, insula, 14 right supramarginal gyrus, bilateral dorsolateral prefrontal cortex, and midcingulate cortex. 15Cerebral small vessel disease and its imaging characteristics were related to gait, and cognition in PD patients.It was found white matter hyperintensity was associated with slow gait speed, decreased cadence, increased stride time, and increased stance phase time.The presence of lacune was associated with poor attention and impaired executive function.It was demonstrated white matter hyperintensity, number of lacunes, and microbleeds were positively correlated with the severity of motor, cognitive, and emotional impairments, while the perivascular space in the basal ganglia was only correlated with cognitive impairments.Moreover, monitoring cerebral blood flow by arterial spin labeling (ASL)-MRI, researchers found hypoperfusion was a predictor of cognitive impairment in PD patients. 16,17The above studies revealed multidimensional imaging markers of comorbid cognitive impairment in PD.However, the relationship between spontaneous brain activity and comorbid cognitive impairment in PD is unclear.
The emerging resting-state functional magnetic resonance (RS-fMRI) technology has brought new methods for collecting brain neural activity.With the development of methodology, the proposal of fractional amplitude of low-frequency fluctuations (fALFF) laid the foundation for characterizing neural activity.fALFF can be applied to analyze brain activity in the state of neurological diseases is widely used to explore the mechanism of neurological diseases, and has been found to have significant repeatability.In conclusion, this study used the RS-fMRI method to detect the brain activity by fALFF in the cognitive brain regions in PD patients, and innovatively found the characteristics of cognitive stratification by comparing the differences in spontaneous brain activity between different cognitive groups.

| Participants
The fMRI data were obtained from the brain imaging shared database − OpenNeuro (https:// openn euro.org/ ), with dataset number ds004392.This was a resting state functional imaging dataset comprising PD patients with different cognitive levels.In addition, the data also included baseline information such as cognitive scores

Conclusion:
The parahippocampal gyrus was the potential brain region for recognizing cognitive impairment in PD, specifically for identifying MCI.Thus, the fALFF of parahippocampal gyrus is expected to contribute to future study as a multimodal fingerprint for early warning.assessing attention, memory, language, and spatial abilities, with a total score of 30 points. 18MoCA played a crucial role in the early detection of cognitive impairments, diagnosis of MCI, and evaluation of treatment efficacy.0][21][22] Furthermore, MCI together with SCI groups is considered to be the cognitively abnormal group.

| Image data pre-processing
The MATLAB 2016b software with the Statistical Parametric Mapping (SPM 12, http:// www.fil.ion.ucl.ac.uk/ spm) package was used for pre-processing the resting state fMRI data.The first 10 volumes were discarded to eliminate the machine instability interference. 23In further processing, slice timing, head motion correction (Friston 24), segment, spatial normalization to a Montreal Neurological Institute (MNI) template (resampling voxel size = 3 mm × 3 mm × 3 mm), smoothing (Gauss kernel of 4 mm with full width and half height), and linear trend removal were performed. 23

| fMRI data post-processing
Rest 1.8 (http:// www.restf mri.net/ forum/ REST_ V1. 8) software was applied for further processing.The time series of each voxel was transformed into the frequency domain and a power spectrum was then obtained.The square root of the power spectrum was calculated at each frequency and the average square root of the power spectrum within the frequency band of 0.01-0.08Hz was retained as the amplitude of low-frequency fluctuation (ALFF). 24ditionally, fALFF was defined as the ratio of the power of each frequency at the low-frequency range (0.01-0.08 Hz) to that of the entire detectable frequency range (0-0.25 Hz). 25 The fALFF could accurately reflect the amplitude levels of brain regions within a specific frequency band, reducing inter-individual differences and the influence of global signals. 25Simultaneously, fALFF mitigates interference from physiological sources like heartbeat and respiration as it normalizes the amplitude for every frequency band. 25erefore, fALFF was selected to enhance data reliability and interpretability to investigate the correlation between spontaneous brain activity and cognition.
The impact of confounding variables on the outcomes was also elucidated in subsequent analyses, predominantly by adjusting for confounding variables (such as age, gender, handedness, education level) versus not making any adjustments.First, voxel-based correlation analysis was conducted to investigate the association between fALFF and MoCA to identify brain regions associated with cognitive impairment (AlphaSim corrected p < 0.01, cluster size >19 voxels).Second, one-way ANOVA and post hoc analyses were applied to compare the differences in brain activity between three cognitive groups (AlphaSim corrected p < 0.01, cluster size >19 voxels).Third, based on the prior knowledge of hippocampus and cognition, we performed correlation analysis between fALFF and MoCA for hippocampal regions to further screen cognitive representative regions of patients with PD.Fourth, a two-sample t-test was used to compare the differences in brain activity in the hippocampal region between the cognitively abnormal and the NC groups.Considering the smaller voxel levels in the hippocampal region, we used a relatively loose correction (AlphaSim corrected p < 0.01, cluster size >1 voxel) aimed at improving the sensitivity of the results.Subsequently, Anchored neurocognitive brain regions from the first three steps were overlapped to generate independent regions of interest (ROI) based on their spatial distribution in a sequential manner.Finally, the fALFF between the three cognitive groups was extracted, based on ROIs from overall and triple group level, for subsequent between-group identification.

| Routine statistical analyses
The statistical analysis and graphical representation were conducted using SPSS 26 (https:// www.ibm.com/ produ cts/ spss-stati stics ), MedCalc (https:// www.medca lcsof tware.com/ ), and GraphPad Prism 9.5 (https:// www.graph pad.com/ ).Initially, the normality of the continuous variables was assessed through the Kolmogorov-Smirnov test (SPSS 26).If the distribution was found to be normal, inter-group comparisons were performed using two-sample t-test or one-way analysis of variance with post-hoc pair-wise comparisons, reported as mean ± standard deviation (SPSS 26 and GraphPad Prism 9.5).In cases of non-normal distribution, the Kruskal-Wallis test was utilized, and the results were presented as median and quartiles (SPSS 26).The count data were compared using the Chisquare test (SPSS 26).
Receiver operating characteristic (ROC) curves were utilized for constructing a predictive measure (MedCalc).The discriminative capability of fALFF was assessed by computing the area under the ROC curve (AUC).An AUC value of 1.0 denotes perfect discrimination, while an AUC value of 0.5 implies a lack of discriminative ability.

| Demographic analysis
A total of 58 PD patients were enrolled in this study, with an average age of 70.03 years, predominantly male, accounting for 67.2% (39 patients), and predominantly right-handed, accounting for 87.9% Patients with SCI were the oldest at 74.4 years in three cognitive groups.In terms of gender, patients with MCI had a higher proportion of males (85.7%) than other groups.There was no significant difference in the education level and the proportion of right-handed in the three cognitive groups (Table 1).

| Correlation analysis between fALFF of whole brain and cognition in PD patients
We analyzed the correlation between whole brain fALFF by voxels and MoCA in PD patients.Without adjusting for confounding factors such as age, gender, handedness, education level, brain regions correlated with cognition included Right Brainstem, Midbrain, Limbic Lobe, Hippocampus, Parahippocampa Gyrus, Amygdala, Fusiform, Occipital Lobe, Parahippocampa Gyrus, Lingual Gyrus, Middle Temporal Gyrus (Figure 1A).After adjusting for confounding factors, brain regions associated with cognition included Right Brainstem, Midbrain, Limbic Lobe, Hippocampus, Parahippocampa Gyrus, Amygdala, Occipital Lobe, Fusiform Gyrus, Parahippocampa Gyrus, Temporal Lobe, Lingual Gyrus, Posterior Cingulate (Figure 1B).We found that after adjusting for confounding factors, only the posterior cingulate was newly identified as a cognitive-related brain region (Table 2).

| Comparison of fALFF between different cognitive groups
One-way ANOVA was used to compare the fALFF in three cognitive groups.Without adjusting for confounding factors such as age, gender, handedness, education level, brain regions with differences in fALFF among three cognitive groups included Midbrain, Limbic Lobe, Parahippocampal Gyrus, Brainstem, Brodmann area 34, Amygdala, Frontal Lobe (Figure 2A, Table S1).After adjusting for confounding factors, brain regions with differences in fALFF among three cognitive groups included the Midbrain, Brainstem, Parahippocampal Gyrus, Limbic Lobe, Amygdala, Brodmann area 34, and Frontal Lobe (Figure 2B, Table S2).We found that whether the confounding factors were adjusted or not, differential brain regions among the three cognitive groups were basically the same, but the voxel number values of the brain regions were different.
We further compared the differences between the three cognitive groups with each other.We found no statistically significant difference in fALFF between PD patients with MCI and NC, regardless of adjustment for confounding factors (Figure 2C,F).
When comparing PD patients with SCI and MCI, without adjusting for confounding factors, there were differences in fALFF including Parahippocampa Gyrus, Limbic Lobe, Midbrain, Brodmann area 34, Brainstem, Amygdala, Frontal Lobe, Hippocampus, Precuneus, Brodmann area 31, Posterior Cingulate, brodmann area 7 (Figure 2E, Table S5).After adjusting for confounding factors, differential brain regions were basically the same, but the voxel number values of the brain regions were decreased, including differences in fALFF of Midbrain, Brainstem, Limbic Lobe,

| Correlation analysis between fALFF of hippocampus and cognition in PD patients
Based on the prior knowledge of hippocampus and cognition, we further analyzed the correlation between hippocampus fALFF by voxels and MoCA in PD patients.We found the hippocampus was a stable representative brain region of cognition, regardless of adjustment for confounding factors (Figure 3, Tables S7 and S8).

| Making ROIs in different brain regions
We overlay the differential brain regions expressed by Figures 1-3  3).

| Comparison of fALFF of 10 ROIs between different cognitive groups
The fALFF of three cognitive groups were collected according to the 10 ROIs in Figure 6, and the differences between groups were compared (Figure 6).When comparing PD patients with MCI and NC, there were differences in fALFF only including ROI 5. When comparing PD patients with SCI and NC, there were differences in fALFF of

| The potential ability to identify cognitive impairment by ROIs
We produce ROC-AUC for the evaluation of the potential of ROIs to identify cognitive impairment.The AUC of fALFF of ROI 5 in distinguishing PD patients with MCI from NC was 0.673 (Figure 7A).The

| fALFF is an effective method to assess brain activity in PD patients
7][28] With the progress of the disease, most PD patients will eventually be diagnosed with dementia.The prevalence of dementia in PD patients was approximately 30%, with an incidence rate of 24.3/1000/year (95% confidence interval, 7.7-58). 6Cognitive impairment was a manifestation in the early stage of dementia.Early detection and intervention of cognitive impairment could improve the quality of life of PD patients. 29,30The research on brain activity associated with cognitive impairment of PD patients had important implications not only for the treatment and management of PD patients but also for further understanding the pathophysiological mechanisms of PD.We used RS-fMRI technology to collect brain activity parameters and applied network perspective to study the differences in brain activity in PD patients and find the early warning methods of PD-MCI diagnosis.
The resting state was a state in which the brain was quiet, relaxed, and awake without performing cognitive tasks, and was the most basic and essential state of various complex states of the brain.
The amplitude of low-frequency fluctuation (ALFF) was a signal intensity of the blood oxygen level dependence (BOLD) sequence, which was used to describe the brain activity intensity of a single voxel and was an important feature in describing resting-state images. 31In order to reduce the sensitivity of ALFF to physiological noise and improve the sensitivity and specificity of detecting spontaneous brain activity, the fractional amplitude of low-frequency fluctuations (fALFF) was used in our research.
From the perspective of brain activity, the use of ALFF to study non-motor characteristics of PD patients was the hot research.In the study of anxiety characteristics in PD, Zhang study found that the fALFF of the left cerebellum, cerebellum posterior lobe, bilateral temporal cortex, and brainstem were higher, while the fALFF of bilateral inferior gyrus, bilateral basal ganglia areas, and left inferior parietal lobule were lower.Right, precuneus, and left caudate were correlated with the Hamilton Anxiety Scale. 32By monitoring different brain regions ALFF, Criaud found that anxiety in PD was associated with the over-activation of the amygdala and impaired inter-relationship of regions involved in behavior (i.e.medial prefrontal cortex, insula) and motor control (i.e.basal ganglia). 33The fALFF

TA B L E 3
Monitoring ALFF in the bilateral primary motor cortex, the occipital cortex, the cerebellum, and the basal ganglia could predict the effect of dopaminergic therapy in PD patients. 35

| The hippocampus is the important brain region for cognitive impairment in PD
We analyzed the correlation between whole brain fALFF by voxels and cognition in PD patients.We found similar results in brain regions to previous studies, including the hippocampal structure, limbic lobe, amygdala, occipital lobe, etc. 36,37 These results indicated that the above brain regions were closely related to cognition, attention, and memory. 38out 26.7% (18.9%-38.2%) of non-demented PD patients were considered MCI, 39 and an even higher proportion of 40% was considered MCI. 40In order to find the characteristic brain regions of PD-MCI, we compared fALFF between PD patients with MCI and NC.Unfortunately, there was no statistically significant difference in fALFF between the two groups regardless of adjustment for confounding factors.A previous study demonstrated that compared with PD-NC, PD-MCI showed significantly increased ALFF in the right inferior frontal gyrus and left fusiform gyrus. 31sults found that hippocampal structure was not only a brain region related to cognition in PD patients but also a differential fALFF brain region in PD-SCI.The hippocampus was closely related to learning, memory, emotional response, and other brain neural activities, and it was also an important target for research on cognitive impairment in PD patients because the temporal lobe has been identified as crucial for encoding and memory consolidation. 41,424][45] We innovatively analyzed the correlation between hippocampus fALFF by voxels and cognition in PD patients.It was found that regardless of confounding factors such as age, gender, handedness, education level, the hippocampal region was a stable representative brain region of cognition.

| Feasibility of parahippocampal gyrus in predicting cognitive impairment in PD patients
The purpose and methods of the current research were limited to speculating the effect of hippocampal atrophy on cognitive impairment in PD patients from the perspective of imaging.Gray matter atrophy of the hippocampus in PD patients resulted in impairment of functional connectivity, associated with different stages of cognitive impairment. 46,47At present, some researchers explored the function of the hippocampus in PD patients from the perspective of brain activity.In the study of non-motor symptoms of PD patients, Xu found an abnormal degree of centrality value in the hippocampus, and parahippocampus was observed separately in the conventional band and in the slow-4 band in PD with apathy, which confirmed the density of internal functional connections in hippocampal decreased. 48Zi performed statistical differences in fALFF between patients in PD with excessive daytime sleepiness and analyzed the changes in the functional connection within the whole brain.It was found that the functional connectivity in the right hippocampus/parahippocampal was decreased, which resulted in damage to the dopaminergic circuits in the limbic system and subsequently inhibited the arousal mechanism. 49However, Wang found that ALFF was elevated in the hippocampal structure in PD patients, and the hippocampus was an important structure for studying PD. 50 comparing the fALFF of three cognitive groups, we found that the parahippocampal gyrus corresponding to the ROI 5 brain region was the different brain region of the three cognitive groups, and it was also the only brain region to identify MCI from NC.The parahippocampal gyrus was an important and active region of the limbic system, whose main functions included creating memories and recalling visual scenes.The research found that impaired function of the parahippocampal gyrus in PD-MCI led to memory impairment 31 and showed that parahippocampal gyrus was essential for discovering connections between things and finding target objects. 51 also found that the fALFF of parahippocampal gyrus of PD patients with SCI decreased more significantly than that of PD patients with MCI and NC.Previous evidence of hippocampal atrophy combined with our results of decreased parahippocampal gyrus activity might account for and predict the cognitive impairment in PD patients.Similarly, in the study of vascular dementia and Alzheimer's disease, it was found that abnormal brain activities in different parts of brain were related to pathological processes, which provided theoretical support for explaining the mechanism of cognitive impairment in imaging perspective. 52,53The study found that sharp ripples are a manifestation of interactions between the hippocampus and cortical loops, which are closely linked to cognition and memory. 54wever, whether similar cognitive loop abnormalities exist in the PD co-morbid cognitively impaired population warrants elucidation in terms of functional versus causal connectivity.
In order to explore the ability of brain activity in the parahippocampal gyrus to identify cognitive impairment, we found that the fALFF of parahippocampal gyrus of PD patients with MCI decreased more significantly than that of PD patients with SCI, which indicated that the brain activity of parahippocampal gyrus decreased, and its functional impairment was closely related to cognitive impairment.
The fALFF of parahippocampal gyrus was 0.673 of AUC for identifying MCI and NC, 0.920 of AUC for identifying SCI and NC, and 0.869 of AUC for identifying SCI and MCI.From our perspective, the ability of fALFF of parahippocampal gyrus in identifying SCI from NC and MCI was acceptable, but the AUC for identifying the most important MCI from NC was 0.673, and its ability to identify was ordinary.This may be a potential biomarker worth combining with other indicators.

| Limitations need to be discussed
Firstly, common and typical results of brain activity in PD patients were found based on the current sample size, but the robustness of the results was worth further verifying in a larger sample size.
Secondly, brain activity in the parahippocampal gyrus was found to be an effective region for cognitive stratification in PD patients, but the brain regions (functional connectivity) affected by the abnormal parahippocampal gyrus activity were still unclear.It was suggested that regional homogeneity and functional connectivity methods were recommended to explore the relationship between brain regions in future studies.In addition, it is a consensus that structure determines function.From the perspective of brain activity, we showed the new discoveries, however, it was beneficial to further understand the pathological mechanism of PD patients evolving into different cognitive states by multimodal methods finding the changes between structure and function.Finally, abnormal brain activity in the parahippocampal gyrus could early identify PD-MCI and became an early warning indicator, however, its ability to identify was ordinary.We suggested that in further research, it was necessary to combine other biochemical indicators, cerebral blood flow, and other parameters to comprehensively analyze.

| CON CLUS IONS
According to the research, the cognitive impairment of PD patients was associated with age and gender.By comparing the fALFF of three cognitive groups, discovery of the parahippocampal gyrus as an anchored differential brain region and it was also the only brain region to identify MCI from NC.It was revealed parahippocampal gyrus was the prospective brain region for recognizing cognitive impairment in PD, specifically for identifying MCI.In view of the ordinary ability of fALFF of parahippocampal gyrus to identify the most important MCI from NC, it was necessary to combine fALFF of parahippocampal gyrus with other indicators for early warning study.

ACK N OWLED G M ENTS
We thank "CNS Neuroscience & Therapeutics" for providing such an excellent academic exchange platform.

FU N D I N G I N FO R M ATI O N
The funding was provided by the Fundamental Research Funds for the Central Universities (2022CDJYGRH-015); the National Natural Science Foundation of Chongqing (CSTB2022NSCQ-MSX1503);

K
E Y W O R D S cognitive impairment, fractional amplitude of low-frequency fluctuation, hippocampal regions; spontaneous brain activity, Parkinson's disease | 3 of 13 CHEN et al.
the hippocampus region.To reveal the baseline level of spontaneous brain activity in the hippocampus of the PD patients with NC, we further compared the differences in fALFF in the hippocampus region between NC and cognitively abnormal patients and quantified the fALFF values.Specifically, Figure4presents the fALFFreduced (Figure4A,B) and -enhanced (Figure4C,D) brain regions of patients with cognitively abnormal, both uncorrected (Figure4A,C) and corrected (Figure4B,D) for confounders.The fALFF quantification showed that compared to the cognitively abnormal group,

8 regions including ROI 1 ,
ROI 2, ROI 3, ROI 5, ROI 6, ROI 7, ROI 8 and ROI 10.When comparing PD patients with SCI and MCI, there were differences in fALFF of 7 regions including ROI 1, ROI 2, ROI 3, ROI 5, ROI 6, ROI 8, and ROI 10.ROI 5 was the only region with statistically significant differences in fALFF among the three cognitive groups.We found that ROI 5 had the highest fALFF in PD patients with NC and the lowest fALFF in patients with SCI.

F I G U R E 3 F I G U R E 4
Correlation between hippocampus fALFF level and MoCA in PD patients.(A) The hippocampus was a stable representative brain region of cognition.Related regions in the hippocampus without adjusting for confounding factors.(B) The hippocampus was a stable representative brain region of cognition.Related regions in the hippocampus after adjusting confounding factors.Quantitative results of fALFF values reveal differences in spontaneous brain activity in the hippocampal region between the cognitively abnormal and the NC groups (*p < 0.05; fALFF, fractional amplitude of low-frequency fluctuation).(A) The fALFF-reduced brain regions in the hippocampus of patients with cognitively abnormal (uncorrected for confounders).(B) The fALFF-reduced brain regions (corrected for confounders).(C) The fALFF-enhanced brain regions uncorrected for confounders.(D) The fALFF-enhanced distribution of the brain regions corrected for confounders.The AUC of fALFF of ROI 1, ROI 2, ROI 3, ROI 5, ROI 6, ROI 8, ROI 10 in distinguishing PD patients with SCI from MCI was 0.850, 0.910, 0.907, 0.869, 0.926, 0.776, 0.702, respectively (Figure7D,E).The results revealed that parahippocampal gyrus (ROI 5) was the only region that identified PD patients with MCI and NC.The paracentral lobule, postcentral gyrus (ROI 1) was the best region that identified PD patients with SCI and NC, with an AUC of 0.941.The midbrain, hippocampus, and parahippocampa gyrus (ROI 6) was the best region that identified PD patients with SCI and MCI, with an AUC of 0.926.

Regions Peak MNI Peak intensity AlphaSim_ p value x/y/z
TA B L E 2Correlation between fALFF at the voxel level across the whole brain in patients with PD and MoCA scores.